Evidence Synthesis for Decision Making 4: Inconsistency in Networks of Evidence Based on Randomized Controlled Trials

Sofia Dias*, Nicky J Welton, Alex J. Sutton, Deborah M Caldwell, Guobing Lu, A E Ades

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

490 Citations (Scopus)
554 Downloads (Pure)


Inconsistency can be thought of as a conflict between "direct" evidence on a comparison between treatments B and C and "indirect" evidence gained from AC and AB trials. Like heterogeneity, inconsistency is caused by effect modifiers and specifically by an imbalance in the distribution of effect modifiers in the direct and indirect evidence. Defining inconsistency as a property of loops of evidence, the relation between inconsistency and heterogeneity and the difficulties created by multiarm trials are described. We set out an approach to assessing consistency in 3-treatment triangular networks and in larger circuit structures, its extension to certain special structures in which independent tests for inconsistencies can be created, and describe methods suitable for more complex networks. Sample WinBUGS code is given in an appendix. Steps that can be taken to minimize the risk of drawing incorrect conclusions from indirect comparisons and network meta-analysis are the same steps that will minimize heterogeneity in pairwise meta-analysis. Empirical indicators that can provide reassurance and the question of how to respond to inconsistency are also discussed.

Original languageEnglish
Pages (from-to)641-656
Number of pages16
JournalMedical Decision Making
Issue number5
Publication statusPublished - Jul 2013


  • network meta-analysis
  • inconsistency
  • indirect evidence
  • Bayesian
  • immunodeficiency virus prevalence
  • treatment comparison meta-analysis
  • mixed treatment comparisons
  • competing interventions
  • surveillance data
  • validity
  • bias


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